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AI Solutions Architect: Design, Build & Lead Enterprise AI Systems

Architect production AI systems that scale, secure, and deliver ROI

Go beyond building models — learn to architect the complete AI systems that power enterprise products. From data infrastructure and model selection to LLM deployment patterns, multi-agent systems, security, governance, and stakeholder communication. The course every senior engineer and architect needs as AI becomes the core of every technology stack.

Enroll Now — $2,499 See Curriculum
110
Total Hours
12
Weeks
8
Modules
~9
Hrs/Week
Complete Course Structure & Table of Contents
📡
Live instructor-led delivery · classes run 3 days a week. The topics below are covered in live sessions; recorded versions will be available after class delivery.
Week 1-2
AI Architecture Fundamentals & Design Principles
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The AI Architect's Role in the Modern Enterprise
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AI System Design Patterns: Monolith vs Microservices for AI
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The Build vs Buy vs Fine-Tune vs RAG Decision Framework
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Cost Modeling for AI Systems: Compute, Storage & API Costs
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Activity: Audit Your Current Tech Stack for AI Integration Points
Cloud ML Training Lab
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Lab 1: Map a Sample Enterprise System to an AI Architecture Blueprint
Cloud ML Training Lab
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Workbook: Complete the AI Decision Framework for 3 Real Use Cases
Cloud ML Training Lab
Week 3
Data Architecture for AI
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Data Pipelines for ML: Batch vs Streaming vs Lambda Architecture
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Feature Stores: Feast, Tecton & In-House Patterns
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Vector Databases: Pinecone, Weaviate, Qdrant & pgvector Compared
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Data Versioning, Lineage & Quality with DVC and Great Expectations
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Lab 2: Design and Implement a Feature Store for a Recommendation System
RAG & LLM Pipeline Lab
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Lab 3: Benchmark 3 Vector Databases on a Production RAG Query Workload
RAG & LLM Pipeline Lab
Week 4
Model Selection & Evaluation Framework
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Choosing Between LLMs, Fine-Tuned Models & Classical ML
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The Model Evaluation Matrix: Accuracy, Latency, Cost & Compliance
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LLM Gateway Patterns: Routing, Fallback & Cost Control
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Model Cards, Datasheets & Governance Documentation
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Lab 4: Build a Model Benchmarking & Comparison Pipeline
Cloud ML Training Lab
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Workbook: Write an Architecture Decision Record (ADR) for 2 Model Selection Scenarios
Cloud ML Training Lab
Week 5-6
LLM Architecture Patterns
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RAG Architecture Deep Dive: Chunking, Embedding & Retrieval Strategies
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Advanced RAG: Reranking, HyDE & Hybrid Search
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Multi-Agent System Architecture: Orchestrators, Subagents & Tool Use
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Prompt Management Systems & Version Control for Prompts
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Lab 5: Design and Implement a Production-Grade RAG Architecture
RAG & LLM Pipeline Lab
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Lab 6: Build a Multi-Agent Orchestration System with Supervisor & Worker Agents
RAG & LLM Pipeline Lab
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Mini Project 1: LLM-Powered Enterprise Knowledge Base — Architecture to Deployment
RAG & LLM Pipeline Lab
Week 7-8
AI Infrastructure & MLOps at Scale
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Kubernetes for ML Workloads: Kubeflow, Argo & GPU Scheduling
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Model Serving at Scale: Triton Inference Server, vLLM & BentoML
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A/B Testing and Shadow Deployment Strategies for AI Systems
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Cost Optimization: Caching, Request Batching & Model Distillation
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Lab 7: Deploy a Scalable Model Serving Cluster with Cloud Load Balancing
MLOps & Deployment Lab
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Lab 8: Implement Shadow Mode Deployment for an LLM Application
MLOps & Deployment Lab
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Mini Project 2: Multi-Model Serving Infrastructure with Cost Monitoring Dashboard
MLOps & Deployment Lab
Week 9
AI Security, Governance & Compliance
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AI Security Architecture: Prompt Injection, Model Theft & Data Poisoning
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Access Controls for AI Systems: RBAC for Models, APIs & Training Data
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Compliance Frameworks: EU AI Act, NIST AI RMF & SOC 2 for AI
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Red Teaming AI Systems: Adversarial Testing Methodology
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Lab 9: Threat Model a GenAI Application Using the STRIDE Framework
Cloud ML Training Lab
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Workbook: Draft an AI Governance Policy & Risk Register for Your Organization
Cloud ML Training Lab
Week 10
Presenting AI Architecture to Stakeholders
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Architecture Decision Records for AI: Format, Review & Sign-Off Process
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Building the Business Case: Quantifying AI ROI for the Board
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Communicating Technical Risk to Non-Technical Leadership
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AI Roadmap Development: Sequencing for Speed, Safety & Maximum Value
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Activity: Present an ADR to a Mock Executive Committee (peer-reviewed)
Code Server Lab
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Workbook: Draft Your Organization's 12-Month AI Architecture Roadmap
Code Server Lab
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Forum: Peer Architecture Review — Give and Receive Structured Feedback
Code Server Lab
Week 11-12
Capstone: Design a Complete Enterprise AI System
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Capstone Brief: Full-Stack AI Architecture for a Real Business Problem
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AI Architect Interview Preparation: System Design Questions & Patterns
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Cloud AI Architect Certification Paths: GCP, AWS & Azure
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Capstone: End-to-End AI Architecture Document (Phases 1-4): Problem Scoping, Data Architecture, Model Selection & Infrastructure Design
Cloud ML Training Lab
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Capstone: Architecture Presentation to Panel (recorded & peer reviewed)
Cloud ML Training Lab
Live Instructor-Led Batches

Learn with a cohort — live Zoom sessions, Q&A, and lifetime access to recordings.

🐦
Early Bird — 5% off when you enroll 10+ days before your batch starts. Discount auto-applies at checkout. No code needed.
upcoming 🐦 Early Bird 5% off

AI Solutions Architect: Design, Build & Lead Enterprise AI Systems — June Cohort 2026

Instructor: AI Labs Instructor
  • 📅Jun 15, 2026 – Sep 6, 2026
  • 🕐Tuesdays & Thursdays, 7–9 PM CST
$2,499 $2,374.05 You save $125
upcoming 🐦 Early Bird 5% off

AI Solutions Architect: Design, Build & Lead Enterprise AI Systems — July Cohort 2026

Instructor: AI Labs Instructor
  • 📅Jul 15, 2026 – Oct 6, 2026
  • 🕐Tuesdays & Thursdays, 7–9 PM CST
$2,499 $2,374.05 You save $125
Ready to start?

$2,499

Online lectures + Cloud lab sessions + Architecture design workshops

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Frequently asked questions about this program

What level is the AI Solutions Architect program? +
Advanced. Total program length is 12 Weeks (110+ Hours of combined live instruction and lab time).
What prerequisites do I need? +
2+ years software engineering or data engineering experience; Familiarity with cloud platforms (GCP, AWS, or Azure); Basic understanding of ML concepts; System design fundamentals
Does this course include hands-on lab work? +
Yes. This program includes hands-on lab time in 3 cloud lab environments provisioned by AI Labs. Every student gets a personal cloud workspace plus on-prem workstation access at our Houston Training Center.
Is this delivered online or in person? +
Both. The default delivery is Online lectures + Cloud lab sessions + Architecture design workshops. In-person sessions are available at our Houston Training Center for any student who prefers on-site delivery.
What roles does this program prepare me for? +
AI Solutions Architect, Principal AI Engineer, Head of AI Engineering, Chief AI Officer.
Do I receive a certificate at the end? +
Yes. Every program ends with a capstone project and a verifiable AI Labs completion certificate. Certificates are issued via our LMS and include the capstone work as a portfolio link.
How much does the program cost and are payment plans available? +
Program tuition is $2,499. Most students use our 2-installment plan (50% at enrollment, 50% midway through). Enterprise + nonprofit pricing is available — contact us for a quote.

Labs used in this course

Hands-on environments you'll spin up during the program.

Jupyter Notebook Lab

Standard Python development environment for data exploration, visualization, and introductory ML. Pre-configured with Python 3.11, NumPy, Pa…

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Code Server Lab

Full-featured VS Code IDE in your browser — with integrated terminal, file tree, git, extensions, and everything needed for real software en…

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